01
Task planning
The agent breaks a request into steps, works out what it needs and keeps track of where each task is.
AI Agent Development
Custom AI agents that take work off your team's plate.
AI agent development for multi-step tasks that span several systems.
We build AI agents that gather information, check your records, prepare the next action and ask a person to approve it. Each agent is scoped to one job, uses only the tools you allow and logs what it did.
- Only the tools you approve
- A person approves key actions
- Every run logged
No upfront consultation fee. Free discovery and a free tailored proposal.
TaskNew supplier request: Supplier B Sdn Bhd
Request read from the purchasing inboxDone- Registration and contact details retrievedDone
Checked against the supplier master listNo duplicate- Recommendation drafted with sourcesNeeds approval
- Supplier record created in the ERPAfter approval
Approval needed: add Supplier BThe procurement manager reviews the findings and sourcesReviewApprove
The challenge
The work happens between the apps.
Many tasks aren't hard, just scattered. Someone opens five systems, copies details between them and chases colleagues for updates before anything moves.
Done by hand
With an AI agent
Switching between apps to finish one task
The agent gathers what it needs across your tools
Copying the same details from tool to tool
One shared task record that every step reads
Chasing colleagues for status updates
Status updates and exception alerts as it runs
Every request researched from scratch
Retrieved from your records and approved sources
Decisions made without the full picture
A drafted recommendation, with sources, to approve
What we build
Agents that finish the task.
A chatbot answers a question. An agent works through the steps: it plans, uses the tools you've approved and stops for a person where it matters.
02
Knowledge retrieval
It works from your policies, records and approved sources, and links each fact back to where it was found.
03
Actions in your tools
Your systems become permitted actions, such as reading a record, creating a draft or posting an update. Nothing outside that list.
04
Document handling
Reads forms, contracts and attachments, pulls out what the task needs and files them in the right place.
05
Human approvals
Sending, paying, changing or deleting records waits for a named person, with the agent's reasoning beside it.
06
Monitoring and audit trail
Each run records its steps, the tools used and the decisions made. Failures are retried or alerted, not guessed around.
See how it works
How an agent works through a task.
Rules decide what the agent may do. AI reads, plans and drafts, and a person approves anything that changes your records or leaves the business.
See it with
-
Step 1 of 4Task received
Purchasing emails a request to add Supplier B
-
Step 2 of 4Plans and gathers
Finds the supplier's details and reads the attached forms
-
Step 3 of 4Checks and drafts
Checks the master list for duplicates and drafts a recommendation
-
Step 4 of 4Action or approval
Manager approves, and the supplier record is created
Updated in your systemsHanded to a person
AI reads and decides within your rulesAutomated stepsYour team, when a person is needed
Connected systems
Tools your agent is allowed to use.
An agent is only as useful as the systems it can reach. Each connection is set up as a specific permitted action, not open access.
Requests and messages
- Gmail
Slack- Web forms
Knowledge and files
Google Drive
Notion- Policies
Your AI agentTasks, tools and approvals
CRM and records
HubSpot
Salesforce- Google Sheets
Airtable
Models and orchestration
Claude
OpenAI
Gemini
n8n
Each connection depends on the system's API, your licences and the permissions you grant. We scope every tool to the least access the task needs.
API Integrations →
Architecture and controls
Every action passes through your rules.
Behind each agent is a simple chain: the input, the agent's reasoning, a list of permitted tools, validation, approval where needed and then the output. This is how each link is kept in bounds.
- Permissions
- Each tool is scoped to the least access the task needs. The agent can't reach systems or records outside its list.
- Validation and retries
- Outputs are checked against rules before they're used. A failed tool call is retried, then the run stops and alerts a person.
- Guardrails
- Documents, web pages and emails are treated as data, not instructions. Missing data or low confidence stops the run.
- Audit trail
- Each run records the inputs, steps, tools called and who approved what. Data handling, AI providers and hosting are confirmed per project.
Input: purchasing emailReceived
Tool: read supplier masterPermitted
Tool: delete supplierNot permitted
Output: validation rulesPassed
Action: create supplierAwaiting approval
Actions that change records wait for a named person.
Our work
Agent-style systems, in production.
Two live client research systems where agents source, enrich and draft and people approve, plus solution showcases for contract review, CV screening, lead lists and quotations.
Client workAll client workView all →
Capital markets · Research & outreachHow a Capital-Raising Team Cut Investor Research from 15 Minutes to 170%shorter deal pipeline timeline15×faster than by handRead the case study →
Private credit · Company intelligenceHow a Private Credit Firm Researches a Target Desk in About 7 Minutes~7 minper desk, unattended90%+coverage on target desksRead the case study →
Solution showcasesAll solution showcasesView all →
Law Firms
AI contract review tool for your legal playbook
Contracts checked clause by clause against your playbook, with cited issues ready for a lawyer to review and decide.
See the workflow →
Recruitment
Interview scheduling software with AI CV screening
CVs summarised against the job's requirements and interviews self-booked, while recruiters make every decision.
See the workflow →
Sales Teams
Google Maps scraper for B2B lead lists
One sentence in, a list of local businesses with business contacts out, de-duplicated and reviewed before import.
See the workflow →
Manufacturing
Quotation software and RFQ automation
Multi-item RFQs read from email, matched to your catalogue and priced by your rules, ready for sales to approve.
See the workflow →
E-Commerce
Shopify inventory management and dashboard
Competitor prices, stock forecasts and profit in one view, with restock orders drafted for the owner to approve.
See the workflow →
How we work
From one task to a working agent.
- 01
Task discovery
We pick one task, map how it's done today and agree what a good result looks like, using real examples.
- 02
Tool and control design
We agree the tools the agent may use, what it may do on its own and where a person approves.
- 03
Build and evaluate
We build the agent and test it against your examples, including awkward ones, before it touches live work.
- 04
Pilot and expand
We run it on live work with close review, tune it, then add tasks one at a time.
Every project has an agreed scope and acceptance criteria before the build starts.
Project scope
Scoped around one task first.
Every agent is different, so we scope yours around the task, the tools it needs and the approvals you want. These factors shape it.
- Task complexity
- One task with a few steps, or several agents handing work to each other.
- Tools and systems
- How many systems the agent reads from or acts in, and whether they have APIs.
- Knowledge sources
- Clean records, or documents that need preparing so the agent can find them.
- Approval design
- Suggest-only agents are lighter than ones that act without a sign-off.
- Volume and speed
- How many tasks run each day, and how quickly each one needs to finish.
- Security and hosting
- Cloud or private deployment and audit needs. AI usage fees are separate.
Guides
Guides on this
Solutions
How to build an AI voice agent with Vapi end to end
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Guides
AI chatbot vs AI agent: what buyers really need to know
8 min read
Solutions
How to build AI agents in n8n for real business workflows
8 min read
Solutions
AI agents for business: practical use cases that work today
9 min read
All insights
FAQ
AI agent questions.
Straight answers about scope, data, ownership and support. Can’t find yours? Ask us directly below.
What is an AI agent, and how is it different from a chatbot?
A chatbot mainly answers questions in a conversation. An AI agent works through a task: it plans the steps, reads your records and approved sources, uses permitted tools such as your CRM or spreadsheets, and prepares or takes the next action. Where an action matters, it stops for a person to approve. If you mainly need to answer customers, see AI chatbot development.
What tasks can a custom AI agent perform?
Multi-step work that follows a pattern but needs some reading or judgement: researching a company or supplier, triaging requests, gathering details from several systems, drafting summaries and recommendations, and preparing record updates. Tasks with fixed rules and no judgement are often better as plain workflow automation.
What can a custom AI agent connect to and take actions in?
Most systems with an API or a permissioned automation layer, subject to access, licensing and scope. We connect agents to CRMs like HubSpot, workspaces like Notion, Slack and Google Drive, spreadsheets and databases, and orchestration platforms like n8n, Make and Zapier. Private databases are reached through read-only or narrowly scoped accounts. Each tool is set up as a specific action with scoped permissions, so the agent can only do what you allow.
Can a person approve actions before they happen?
Yes. You decide in scoping which actions need approval. Typically an agent can read, search, summarise and draft on its own, while sending messages, changing records, spending money or deleting anything waits for a named person. Limits are set per tool and can be tightened later.
What happens if the agent makes a mistake?
No agent is error-free, so the design assumes mistakes can happen. Outputs are checked against rules before they're used, the agent records which data it used, and missing data or low confidence stops the run and alerts a person rather than guessing. Approval steps catch errors before they reach your records, and the run log shows what happened so it can be corrected.
What technologies and models do you build AI agents with?
We are model-neutral. We build on models such as Claude, OpenAI's models and Gemini, with orchestration, function (tool) calling, memory and retrieval (RAG) so the agent works from your own sources. For tool execution we use n8n, Make or Zapier and direct API integrations. We choose the stack that fits your accuracy, speed, cost and data-sensitivity needs.
Can the agent run in a private environment?
Often, yes. We minimise the data each agent touches, keep secrets and API keys out of prompts, and can design hosted, bring-your-own-key or private setups, including keeping the data layer on your own infrastructure. If the agent calls a cloud AI model, the data it sends is mapped and agreed during scoping. Data handling, AI providers and hosting are confirmed per project.
Do we own the agent, its code and prompts after handover?
You own the bespoke deliverables built for your project under our agreement, such as the prompts, tool definitions and configuration we create for you. Framworq keeps its pre-existing tools and reusable components, with the rights your team needs to use the system. It runs in your accounts and infrastructure where possible, and we hand over documentation so your team can run and extend it.
Are ongoing support fees mandatory?
No. We build in logging and error alerts and hand over documentation so your team can run the agent. If you prefer, you can choose a separate plan covering monitoring, prompt and tool tuning, and model upgrades.
Next step
Have a process that needs more than a chatbot?
Tell us about a task that takes your team across several systems. We'll show you where an agent fits and where a person should approve, and prepare an initial plan around it.
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